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Kaizer AM, Belli HM, Ma Z, Nicklawsky AG, Roberts SC, Wild J, Wogu AF, Xiao M, Sabo RT. Recent innovations in adaptive trial designs: A review of design opportunities in translational research. J Clin Transl Sci 2023; 7:e125. [PMID: 37313381 PMCID: PMC10260347 DOI: 10.1017/cts.2023.537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/29/2023] [Accepted: 04/17/2023] [Indexed: 06/15/2023] Open
Abstract
Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.
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Affiliation(s)
- Alexander M. Kaizer
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Hayley M. Belli
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Zhongyang Ma
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Andrew G. Nicklawsky
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Samantha C. Roberts
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jessica Wild
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Adane F. Wogu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Mengli Xiao
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Roy T. Sabo
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
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Jazić I, Liu X, Laird G. Design and analysis of drop-the-losers studies using binary endpoints in the rare disease setting. J Biopharm Stat 2021; 31:507-522. [PMID: 34053399 DOI: 10.1080/10543406.2021.1918139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
The drop-the-losers design combines a phase 2 trial of k treatments and a confirmatory phase 3 trial under a single adaptive protocol, thereby gaining efficiency over a traditional clinical development approach. Such designs may be particularly useful in the rare disease setting, where conserving sample size is paramount, and control arms may not be feasible. We propose an unconditional exact likelihood (UEL) testing and inference procedure for these designs for a binary endpoint using small sample sizes, comparing its operating characteristics to existing methods. Additional practical considerations are evaluated, including the choice of stagewise sample sizes and effect of ties.
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Affiliation(s)
- Ina Jazić
- Department of Biostatistics, Vertex Pharmaceuticals, Boston, U.S.A
| | - Xiaoyan Liu
- Department of Biostatistics, Boston University, Boston, U.S.A
| | - Glen Laird
- Department of Biostatistics, Vertex Pharmaceuticals, Boston, U.S.A
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3
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Sverdlov O, Ryeznik Y, Wong WK. Opportunity for efficiency in clinical development: An overview of adaptive clinical trial designs and innovative machine learning tools, with examples from the cardiovascular field. Contemp Clin Trials 2021; 105:106397. [PMID: 33845209 DOI: 10.1016/j.cct.2021.106397] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/28/2021] [Accepted: 04/05/2021] [Indexed: 11/30/2022]
Abstract
Modern data analysis tools and statistical modeling techniques are increasingly used in clinical research to improve diagnosis, estimate disease progression and predict treatment outcomes. What seems less emphasized is the importance of the study design, which can have a serious impact on the study cost, time and statistical efficiency. This paper provides an overview of different types of adaptive designs in clinical trials and their applications to cardiovascular trials. We highlight recent proliferation of work on adaptive designs over the past two decades, including some recent regulatory guidelines on complex trial designs and master protocols. We also describe the increasing role of machine learning and use of metaheuristics to construct increasingly complex adaptive designs or to identify interesting features for improved predictions and classifications.
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Affiliation(s)
- Oleksandr Sverdlov
- Early Development Biostatistics, Novartis Pharmaceuticals Corporation, USA.
| | - Yevgen Ryeznik
- Department of Pharmaceutical Biosciences, Uppsala University, Sweden
| | - Weng Kee Wong
- Department of Biostatistics, University of California Los Angeles, USA
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Stallard N, Hampson L, Benda N, Brannath W, Burnett T, Friede T, Kimani PK, Koenig F, Krisam J, Mozgunov P, Posch M, Wason J, Wassmer G, Whitehead J, Williamson SF, Zohar S, Jaki T. Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19. Stat Biopharm Res 2020; 12:483-497. [PMID: 34191981 PMCID: PMC8011600 DOI: 10.1080/19466315.2020.1790415] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/23/2020] [Accepted: 06/24/2020] [Indexed: 02/06/2023]
Abstract
The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.
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Affiliation(s)
- Nigel Stallard
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Lisa Hampson
- Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland
| | - Norbert Benda
- The Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany
| | - Werner Brannath
- Institute for Statistics, University of Bremen, Bremen, Germany
| | - Thomas Burnett
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Peter K. Kimani
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Franz Koenig
- Section for Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | - Johannes Krisam
- Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany
| | - Pavel Mozgunov
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Martin Posch
- Section for Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | - James Wason
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | | | - John Whitehead
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - S. Faye Williamson
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Sarah Zohar
- INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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Kimani PK, Todd S, Renfro LA, Glimm E, Khan JN, Kairalla JA, Stallard N. Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection. Stat Med 2020; 39:2568-2586. [PMID: 32363603 PMCID: PMC7785132 DOI: 10.1002/sim.8557] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 03/31/2020] [Accepted: 04/06/2020] [Indexed: 02/02/2023]
Abstract
In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two-stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous.
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Affiliation(s)
- Peter K Kimani
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, Reading, UK
| | - Lindsay A Renfro
- Division of Biostatistics, University of Southern California, Los Angeles, CA, USA
| | | | | | - John A Kairalla
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Nigel Stallard
- Warwick Medical School, University of Warwick, Coventry, UK
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Lee KM, Wason J, Stallard N. To add or not to add a new treatment arm to a multiarm study: A decision-theoretic framework. Stat Med 2019; 38:3305-3321. [PMID: 31115078 PMCID: PMC6619445 DOI: 10.1002/sim.8194] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 03/05/2019] [Accepted: 04/17/2019] [Indexed: 12/16/2022]
Abstract
Multiarm clinical trials, which compare several experimental treatments against control, are frequently recommended due to their efficiency gain. In practise, all potential treatments may not be ready to be tested in a phase II/III trial at the same time. It has become appealing to allow new treatment arms to be added into on‐going clinical trials using a “platform” trial approach. To the best of our knowledge, many aspects of when to add arms to an existing trial have not been explored in the literature. Most works on adding arm(s) assume that a new arm is opened whenever a new treatment becomes available. This strategy may prolong the overall duration of a study or cause reduction in marginal power for each hypothesis if the adaptation is not well accommodated. Within a two‐stage trial setting, we propose a decision‐theoretic framework to investigate when to add or not to add a new treatment arm based on the observed stage one treatment responses. To account for different prospect of multiarm studies, we define utility in two different ways; one for a trial that aims to maximise the number of rejected hypotheses; the other for a trial that would declare a success when at least one hypothesis is rejected from the study. Our framework shows that it is not always optimal to add a new treatment arm to an existing trial. We illustrate a case study by considering a completed trial on knee osteoarthritis.
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Affiliation(s)
- Kim May Lee
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - James Wason
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.,Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK
| | - Nigel Stallard
- WMS - Statistics and Epidemiology, University of Warwick, Coventry, UK
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Chiu YD, Koenig F, Posch M, Jaki T. Design and estimation in clinical trials with subpopulation selection. Stat Med 2018; 37:4335-4352. [PMID: 30088280 PMCID: PMC6282861 DOI: 10.1002/sim.7925] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 05/23/2018] [Accepted: 07/06/2018] [Indexed: 11/10/2022]
Abstract
Population heterogeneity is frequently observed among patients' treatment responses in clinical trials because of various factors such as clinical background, environmental, and genetic factors. Different subpopulations defined by those baseline factors can lead to differences in the benefit or safety profile of a therapeutic intervention. Ignoring heterogeneity between subpopulations can substantially impact on medical practice. One approach to address heterogeneity necessitates designs and analysis of clinical trials with subpopulation selection. Several types of designs have been proposed for different circumstances. In this work, we discuss a class of designs that allow selection of a predefined subgroup. Using the selection based on the maximum test statistics as the worst‐case scenario, we then investigate the precision and accuracy of the maximum likelihood estimator at the end of the study via simulations. We find that the required sample size is chiefly determined by the subgroup prevalence and show in simulations that the maximum likelihood estimator for these designs can be substantially biased.
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Affiliation(s)
- Yi-Da Chiu
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancashire, UK
| | - Franz Koenig
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Thomas Jaki
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancashire, UK
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Pallmann P, Bedding AW, Choodari-Oskooei B, Dimairo M, Flight L, Hampson LV, Holmes J, Mander AP, Odondi L, Sydes MR, Villar SS, Wason JMS, Weir CJ, Wheeler GM, Yap C, Jaki T. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med 2018; 16:29. [PMID: 29490655 PMCID: PMC5830330 DOI: 10.1186/s12916-018-1017-7] [Citation(s) in RCA: 349] [Impact Index Per Article: 58.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 01/30/2018] [Indexed: 12/11/2022] Open
Abstract
Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.
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Affiliation(s)
- Philip Pallmann
- Department of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF UK
| | | | - Babak Choodari-Oskooei
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | | | - Laura Flight
- Medical Statistics Group, University of Sheffield, Sheffield, UK
| | - Lisa V. Hampson
- Department of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF UK
- Statistical Innovation Group, Advanced Analytics Centre, AstraZeneca, Cambridge, UK
| | - Jane Holmes
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | | | - Lang’o Odondi
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Matthew R. Sydes
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Sofía S. Villar
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - James M. S. Wason
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Institute of Health and Society, Newcastle University, Newcastle, UK
| | - Christopher J. Weir
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Graham M. Wheeler
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cancer Research UK & UCL Cancer Trials Centre, University College London, London, UK
| | - Christina Yap
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, UK
| | - Thomas Jaki
- Department of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF UK
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Kimani PK, Todd S, Stallard N. Estimation after subpopulation selection in adaptive seamless trials. Stat Med 2015; 34:2581-601. [PMID: 25903293 PMCID: PMC4973856 DOI: 10.1002/sim.6506] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2013] [Revised: 12/24/2014] [Accepted: 03/22/2015] [Indexed: 12/30/2022]
Abstract
During the development of new therapies, it is not uncommon to test whether a new treatment works better than the existing treatment for all patients who suffer from a condition (full population) or for a subset of the full population (subpopulation). One approach that may be used for this objective is to have two separate trials, where in the first trial, data are collected to determine if the new treatment benefits the full population or the subpopulation. The second trial is a confirmatory trial to test the new treatment in the population selected in the first trial. In this paper, we consider the more efficient two-stage adaptive seamless designs (ASDs), where in stage 1, data are collected to select the population to test in stage 2. In stage 2, additional data are collected to perform confirmatory analysis for the selected population. Unlike the approach that uses two separate trials, for ASDs, stage 1 data are also used in the confirmatory analysis. Although ASDs are efficient, using stage 1 data both for selection and confirmatory analysis introduces selection bias and consequently statistical challenges in making inference. We will focus on point estimation for such trials. In this paper, we describe the extent of bias for estimators that ignore multiple hypotheses and selecting the population that is most likely to give positive trial results based on observed stage 1 data. We then derive conditionally unbiased estimators and examine their mean squared errors for different scenarios.
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Affiliation(s)
- Peter K. Kimani
- Warwick Medical SchoolThe University of WarwickCoventryCV4 7ALU.K.
| | - Susan Todd
- Department of Mathematics and StatisticsThe University of ReadingRG6 6AXReadingU.K.
| | - Nigel Stallard
- Warwick Medical SchoolThe University of WarwickCoventryCV4 7ALU.K.
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